Novel Cascade Classifier Using Multiresolution Progressive Learning for Device-Free Indoor Localization
IEEE Sensors Letters
Indoor localization of human objects has many important applications nowadays. Here, we propose a new multiresolution device-free indoor localization scheme using wireless radio frequency (RF) fingerprints. In our proposed new device-free approach, all transceiver devices are fixed in an indoor environment, so human targets do not need to carry any transceiver device with them. First, the indoor geometry is divided into several zones, and then, the received signal strength indicators measured by the receiving antennas are input features to our designed innovative machine learning model to identify within which zone the target is. Our proposed machine learning model, i.e., a multiresolution random forest classifier, is composed of a cascade architecture, which integrates and distills learned results over various zoning resolutions. The proposed new multiresolution approach greatly outperforms the existing random forest classifier. The average Euclidean distance error resulting from our proposed new technique is 1.0376 m.
device-free indoor localization, multiresolution (MR) cascade classifier, random forest classifier, received signal strength indicator (RSSI), Sensor applications, wireless radio frequency fingerprinting
Applied Data Science
Prasanga Neupane, Hsiao Chun Wu, Guannan Liu, Weidong Xiang, Jinwei Ye, and Shih Yu Chang. "Novel Cascade Classifier Using Multiresolution Progressive Learning for Device-Free Indoor Localization" IEEE Sensors Letters (2021). https://doi.org/10.1109/LSENS.2021.3119653